1. Field of the Invention
The present invention relates generally to multi-slice computed tomography (CT) imaging systems, and more particularly, to an apparatus and methods of reconstructing an image using iterative techniques.
2. Description of the Prior Art
A computed tomography (CT) imaging system typically includes an x-ray source that projects a fan-shaped x-ray beam through an object being imaged, such as a patient, to an array of radiation detectors. The beam is collimated to lie within an X-Y plane, generally referred to as an “imaging plane”. Intensity of radiation from the beam received at the detector array is dependent upon attenuation of the x-ray beam by the object. Attenuation measurements from each detector are acquired separately to produce a transmission profile.
The x-ray source and the detector array are rotated within a gantry and around the object to be imaged so that a projection angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements, it al., integral projection data, from the detector array at one gantry angle is referred to as a “view”. A “scan” of the object comprises a set of views made at varying projection angles, during one revolution of the x-ray source and detector array.
In an axial scan, the projection data is processed to construct an image that corresponds to a two-dimensional slice taken through the object. For discrete slices, iterative reconstruction of a full field of view may be performed in order to increase image quality. Iterative reconstruction refers to a method which forms an image by repeatedly adjusting an existing estimate according to the quality of a match between measured data and simulated measurements from a current estimate of the image. The quality of the match may also be affected by consideration of the characteristics of the image alone, such as its smoothness and/or satisfaction of a pre-established model. Multiple iterations are performed to create a resulting reconstructed image that approximately matches the acquired projection data. A full set of reconstructed images is referred to as a 3-D reconstruction, since the set is formed into a three dimensional representation of the object with each image pixel or picture element corresponding to a single voxel or volume element in the 3-D reconstruction.
To reduce the total scan time required for multiple slices, a “helical” scan may be performed. Helical scan techniques allow for large volumes to be scanned at a quicker rate using a single photon source. To perform a “helical” scan, the patient is moved along the z-axis, the axis about which the gantry rotates, synchronously with the rotation of the gantry, while data for a prescribed number of slices are acquired. Such a system generates a single helix from a fan beam helical scan. The helix mapped out by the fan beam yields projection data from which images in each prescribed slice may be reconstructed. In addition to reducing scan time, helical scanning provides other advantages such as better use of injected contrast, improved image reconstruction at arbitrary locations, and better three-dimensional images.
Unfortunately, conventional helical scan CT has several associated disadvantages. Three-dimensional reconstruction may be produced by a series of two-dimensional reconstruction slices, acquired for discrete positions or acquired via a continuous scan of the patient along the z-axis. Acquisition of helical scan data for discrete positions is a limitation in further decreasing scan time. For continuous scans, a scan pattern in which the z-position varies linearly with rotation angle is produced. The scan pattern is interpolated to form two-dimensional planar arrays that approximate scan data acquired when translating the table in discrete steps rather than continuous translation. The interpolation of the helical scanned data introduces errors since the interpolated data does not exactly match true projection values. The errors result in artifacts in the reconstructed image, particularly near sharp discontinuities.
Conventional methods for tomographic image reconstruction in single planes from axial mode data may be found in Avinash C. Kak and Malcolm Slaney, “Principles of Computerized Tomographic Imaging,” Classics in Applied Mathematics, 33, SLAM, 2001, ISBN:089871494X, the entire contents and disclosure of which is hereby incorporated by reference, having been applied especially to X-ray CT since the 1970's. One of the earliest iterative methods for reconstruction, algebraic reconstruction technique (ART), is also discussed in Avinash C. Kak and Malcolm Slaney, “Principles of Computerized Tomographic Imaging,” Classics in Applied Mathematics, 33, SIAM, 2001, ISBN:089871494X, the entire contents and disclosure of which is hereby incorporated by reference. References such as L. Shepp and Y. Vardi, “Maximum Likelihood Reconstruction for Emission Tomography,” IEEE Transactions on Medical Imaging, vol. MI-1, no. 2, pp. 113-122, October 1982, and T. Hebert and R. Leahy, “A Generalized EM Algorithm for 3-D Bayesian Reconstruction from Poisson data Using Gibbs Priors,” IEEE Transactions on Medical Imaging, vol. 8 no. 2, pp. 194-202, June 1989, the entire contents and disclosures of which are hereby incorporated by reference, and their references introduce some key elements of statistically based iterative reconstruction. In references such as K. Sauer and C. A. Bouman, “A Local Update Strategy for Iterative Reconstruction from Projections,” IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 534-548, February 1993, and C. A. Bouman and K. Sauer, “A Unified Approach to Statistical Tomography Using Coordinate Descent Optimization,” IEEE Transactions on Image Processing, vol. 5, no. 3, pp. 480-492, March 1996, the entire contents and disclosures of which are hereby incorporated by reference, fundamental concepts are introduced which have here been extended to three-dimensional CT, particularly the numerical method subsequently referred to as “iterative coordinate descent.” Reconstruction has also been performed in single-slice helical scan CT with Bayesian statistical methods, but that work does not include multi-slice scanning. See Marc Allain, Yves Goussard and Jerome Idier, “Approche regularisee en reconstruction tomographique 3D helicoidale,” Proceedings of the 2001 GRETSI Symposium on Signal and Image Processing, 2001, Toulouse, France, the entire contents and disclosure of which is hereby incorporated by reference.
Another method for reducing scan time is referred to as multi-slice helical scan. In multi-slice helical scan, the detector array extends along the z-axis. Typically, the detector array contains multiple rows, with each row corresponding to a different position in z, and a different measured slice. Some of the detector rows measure projections that exist outside an image plane. Scanned data is then interpolated to form two-dimensional planar arrays that approximate scan data acquired from single slice helical scans taken with the table translating in discrete steps rather than continuous translation. The interpolation of the multi-slice helical scanned data introduces errors since the interpolated data does not exactly match true measured data. The errors result in artifacts in the reconstructed image, particularly near sharp discontinuities.
Also in conventional helical scan CT, algorithms tend to modify and warp an image plane in order to match the projection data. The modifications and warping of the imaging plane causes blurring of the reconstructed image.
Sharp discontinuities typically occur near regions with important detail such as an interface of bone and tissue. The sharp discontinuities may also be due to the presence of dense objects, such as metal clips or other dense objects known in the art. The artifacts may therefore obscure important details of the dense objects and sharp discontinuities and may extend radially, obscuring other regions of the reconstruction.
A disadvantage of iterative reconstruction for multi-slice helical scans is that boundary conditions may be difficult to model when the object being scanned extends beyond a scanned range. Regions outside the field of view (FOV) may affect measurements that project obliquely through the object in a z direction, especially for measured values of projections for slices near the boundaries of the FOV. In helical scan CT systems, projections typically pass through more than one plane in the z direction, creating a cross-plane effect, due to detector arrays extending along the z-direction. The cross-plane effect is particularly strong for multi-slice helical CT systems that use relatively high pitch values. Errors in the summation for a particular projection occur when the projection passes through the FOV, but due to the cross plane effect, also passes through regions outside the FOV. Such projections may result in an erroneous value of a summation computed for a corresponding detector. Therefore, iterative reconstruction for an object extending outside the scanned range produces a reconstructed image containing significant artifacts near boundaries of the FOV.
It would therefore be desirable to provide an iterative method of reconstructing an image for a multi-slice helical scan CT imaging system that provides increased scanning speed, accounts for portions of a scanned object outside a scanned range, and minimizes blurring and artifacts in a reconstructed image.